利用高通量植物图像数据进行快速在线植物叶面积变化检测
Rapid online plant leaf area change detection with high-throughput plant image data.
作者信息
Zhan Yinglun, Zhang Ruizhi, Zhou Yuzhen, Stoerger Vincent, Hiller Jeremy, Awada Tala, Ge Yufeng
机构信息
Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE, USA.
Agricultural Research Division, University of Nebraska-Lincoln, Lincoln, NE, USA.
出版信息
J Appl Stat. 2022 Dec 5;50(14):2984-2998. doi: 10.1080/02664763.2022.2150753. eCollection 2023.
High-throughput plant phenotyping (HTPP) has become an emerging technique to study plant traits due to its fast, labor-saving, accurate and non-destructive nature. It has wide applications in plant breeding and crop management. However, the resulting massive image data has raised a challenge associated with efficient plant traits prediction and anomaly detection. In this paper, we propose a two-step image-based online detection framework for monitoring and quick change detection of the individual plant leaf area via real-time imaging data. Our proposed method is able to achieve a smaller detection delay compared with some baseline methods under some predefined false alarm rate constraint. Moreover, it does not need to store all past image information and can be implemented in real time. The efficiency of the proposed framework is validated by a real data analysis.
高通量植物表型分析（HTPP）因其快速、省力、准确和非破坏性的特点，已成为研究植物性状的一项新兴技术。它在植物育种和作物管理中有着广泛的应用。然而，由此产生的海量图像数据给高效的植物性状预测和异常检测带来了挑战。在本文中，我们提出了一个基于图像的两步在线检测框架，用于通过实时成像数据监测和快速检测单株植物的叶面积变化。在一些预定义的误报率约束下，与一些基线方法相比，我们提出的方法能够实现更小的检测延迟。此外，它不需要存储所有过去的图像信息，并且可以实时实现。通过实际数据分析验证了所提框架的有效性。
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